## Mathematical Methods and Models in the Natural to the Life Sciences

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# Dynamics of a Stochastic Cooperative Predator-Prey System with Beddington-DeAngelis Functional Response

**Academic Editor:**Imran Naeem

#### Abstract

Stochastic cooperative predator-prey system with Beddington-DeAngelis functional response is studied. It presents an investigation of dynamic properties of the system. Our results show that there exists a unique positive solution to the system for any positive initial value, and the positive solution is stochastically bounded. Moreover, under some conditions, we analyze global asymptotic stability of the positive solutions. With small environmental noises, the stochastic system is getting more similar to the corresponding deterministic system. Neither of the species in the system will die out. Finally, simulations are carried out to conform to our result.

#### 1. Introduction

As we all know, in mathematical biology, predator-prey system, competitive system, and cooperative system are the three rudimentary and important ecological systems. The dynamic relationship between species has long been and will continue to be a dominant theme in ecology due to its universal existence and importance. It is well-known that predator-prey systems are very important and extensive in the nature fields. One significant component of the predator-prey relationship is predatorâ€™s functional response, that is, the rate of prey consumption by an average predator. There are many significant functional responses in order to model various different situations. In fact, most of the functional responses are prey-dependent; however, some biologists have argued that in many cases, especially when predators have to search for food and therefore have to share or compete for food, the traditional predator-prey systems with prey-dependent functional response fail to model the interference among predators, the functional response should be predator-dependent. In [1, 2], Beddington and DeAngelis proposed the following predator-prey model with Beddington-DeAngelis functional response: Skalski and Gilliam [3] compared statistical evidence from 19 predator-prey systems, and then they claimed that three predator-dependent functional responses (Hassell-Varley, Beddington-DeAngelis, and Growley-Martin) can provide better description of predator feeding over a range of predator-prey abundances. And the Beddington-DeAngelis type functional response was even suitable in some cases.

But most of this work is restricted to predator-prey systems, little has been done for cooperative systems [4, 5]. May [6] suggested the following set of equations: to describe a pair of mutualists.

However, there is often the interaction among multiple species in nature, whose relationship is more complex than those in two species. Therefore, it is more realistic to consider the multiple-species predator-prey systems. In order to continue studying such models, in this paper, we consider a cooperative predator-prey system with Beddington-DeAngelis functional responses at first: where species is the prey of and , , and are cooperative species. All the parameters in system (3) are positive constants.

In fact, population dynamics is inevitably affected by environmental white noise which is an important component in an ecosystem. But the model (3) is deterministic and does not incorporate the effect of environmental noise. May [6] also pointed out the fact that due to environmental fluctuation, the birth rates, carrying capacity, competition coefficients, and other parameters involved in system exhibit random fluctuation to a greater or a lesser extent. Therefore many scholars rewrote the deterministic models as stochastic ones subjected to stochastic noises, for studying the effect of environmental variability on the population dynamics [7â€“9].

The parameters in the real ecosystems are often subject to lots of environmental noises, since they relate to climate, geographical distribution, geological features, human disaster, human intervention, and other environmental factors. Therefore, the logistics and energy flow, in which they are determined by groups, are fluctuating. The oscillation in population biomass is directly manifested as birth and death rates of random perturbation. Currently, one of the main ways considered in the literature to model the effect of the environmental fluctuations in population dynamics is to assume that the most sensitive parameter is the intrinsic growth rate. Thus, in this paper we introduce some stochastic perturbation into the intrinsic growth rate. Therefore, the intrinsic growth rate can be written as an average growth rate adding some small random perturbed terms. In general, by the well-known central limit theorem, the small terms follow some normal distributions, so we can use standard Brownian motions to represent the environmental fluctuations.

In this paper, taking into account the effect of randomly fluctuating environment, we introduce stochastic perturbation into growth rates , , and to become , , and in system, where represents the intensity of the noise and is a standard white noise, namely, is a standard Brownian motion defined on a complete probability space . Then the stochastic system takes the following form: Considering system (4), the initial conditions , , and will be referred to.

#### 2. Global Positive Solutions

Lemma 1. *For any initial value , where , system (4) has a unique positive local solution for almost surely, where is the explosion time.*

*Proof. *Consider the following equations:
on with initial value , , and . The coefficients of (5) satisfy the local Lipschitz condition, thus there is a unique local solution on . Then , , and are the unique positive local solutions with initial value , , and by ItÃ´â€™s formula.

Theorem 2. *For any initial value , there is a unique solution of system (4) on , and the solution will remain in with probability 1.*

*Proof. *According to Lemma 1, we only need to show that . Let be sufficiently large for , , and lying within the interval . For each integer , we define the stopping times
Obviously, is increasing as . Set ; hence, almost surely. Now, we only need to show that . If this statement is false, there is a pair of constants and such that . Thus there exists an integer such that
Define a function : by
The nonnegativity of this function can be seen from and . If , we have
then
where is a positive number. Substituting this inequality into (9), we see that
Integrating both sides of the above inequality from 0 to and then taking the expectations leads to
Set , then we get by inequality (7). Obviously, for every , there are at least , , and which equal either or , then is no less than
It then follows from (9) that
where is the indicator function of , letting , we have that
This completes the proof.

#### 3. Stochastic Boundedness

*Definition 3. *The solution of system (4) is said to be stochastically ultimately bounded, if for any , there is a positive constant , such that for any initial value , the solution of system (4) has the property that

*Assumption A. *For any initial value , there exists such that

Lemma 4. *Assume that Assumption A holds. Let be a positive solution of (4) with any initial value , for all , then
**
where
*

*Proof. *Define the function , for and . By ItÃ´â€™s formula we get
Integrating from 0 to and taking expectations yields
So,
Let , then we have
From (17), we know
which by the standard comparison argument shows that
that is,

Define the function , for and . By ItÃ´â€™s formula we get
Integrating from 0 to and taking expectations yields
So,
Let , then we have
From (17), we know that
which by the standard comparison argument shows that
that is,
Similarly, we can show that
This completes the proof.

Theorem 5. *Assume that Assumption A holds, the solutions of system (4) with initial value are stochastically ultimately bounded.*

*Proof. *If , its norm here is denoted by , then
by Lemma 4, , . is dependent on and is defined by . By virtue of Chebyshev inequality, we can easily obtain that the solution of system (4) is stochastically ultimately bounded.

#### 4. Stochastic Permanence

*Definition 6 (see [10]). *The solution of system (4) is said to be stochastically permanent, if for any , there exist a pair of positive constants and such that for any initial value , the solution of system (4) has the properties that

*Assumption B. *One has

.

Theorem 7. *Under Assumption B, for any initial value , the solution of system (4) satisfies that
**
where is an arbitrary positive constant satisfying
**
and is an arbitrary positive constant satisfying
*

*Proof. *Define for , then
Define for , by ItÃ´â€™s formula, we get
where

Under Assumption B, choosing a positive constant such that it satisfies (38). By ItÃ´â€™s formula, we get
where
then choosing a positive constant such that it obeys (39), by ItÃ´â€™s formula,
where
where
Hence, it implies that there exists a positive constant such that
Then we have
Integrating both sides of the above inequality from 0 to and then taking the expectations leads to
where . So
Since that , where , obviously
as required.

Theorem 8. *Under Assumption B, system (4) is stochastically permanent.*

*Proof. *By Theorem 5, we know that
Now, for any , let . Then by Chebyshevâ€™s inequality, we can obtain the conclusion easily.

#### 5. Global Asymptotic Stability

*Definition 9. *Let be a positive solution of system (4). If we say that is globally asymptotically stable in expectation, it means that any other solution of system (4) has and that we have initial value . That is

Lemma 10 (see [11]). *Suppose that an n-dimensional stochastic process on satisfies the condition
**
for some positive constants , , and . Then there exists a continuous modification of which has the property that for every there is a positive random variable such that
**
In other words, almost every sample path of is locally but uniformly HÃ¶lder continuous with exponent .*

Lemma 11. *Let be a solution of system (4) on with initial value , then almost every sample path of is uniformly continuous on .*

*Proof. *From system (4), we have the following stochastic integral equation
Letâ€‰â€‰ âˆ’ , = , notice that
On the other hand, by the moment inequality (see [12]) for stochastic integrals, we have that for and ,
where . Let , , and , we obtain
where . Then, we have that almost every sample path of is locally but uniformly HÃ¶lder continuous with exponent for every and therefore almost every sample path of is uniformly continuous on . Similarly, we can show that almost every sample path of and is uniformly continuous on .

Lemma 12 (see [13]). *Let be a nonnegative function defined on such that is integrable on and is uniformly continuous on . Then .*

Theorem 13. *If
**
then system (4) is globally asymptotically stable.*

*Proof. *Define
then is a continuous positive function on . A direct calculation of the right differential of , and then applying ItÃ´â€™s formula, we have
Integrating from 0 to and taking expectations yields